Can machine translation of literary texts fool stylometry?
This article uses standard authorship-attribution stylometry to tell machine translations made with DeepL and Google Translate from human translations. This is done using a Burrows-like distance measure procedure of cluster analysis, later visualized through network analysis. Using a corpus of Frenc...
| Publicado en: | Digital Scholarship in the Humanities Vol. 40; no. 1; pp. 268 - 277 |
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| Formato: | Artículo |
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Oxford University Press / USA
Apr2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=184296846&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 184296846 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 2055768X JEO9 jtl: Digital Scholarship in the Humanities issn: 2055768X maglogo: N pubinfo: dt: Apr2025 vid: 40 iid: 1 pid: 622 pub: Oxford University Press / USA artinfo: ui: 184296846 10.1093/llc/fqaf010 ppf: 268 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.2MB tig: atl: Can machine translation of literary texts fool stylometry? aug: au: Rybicki, Jan affil: Department of Philology, Institute of English Studies, Jagiellonian University, 31-007 Kraków, Poland su: Machine translating Literature translations Warping machines Canon (Literature) Stylometry sug: subj: Machine translating Literature translations Warping machines Canon (Literature) Stylometry keyword: delta distance measure dynamic time warping distance French-to-English literary translation machine translation sentence length distribution stylometry ab: This article uses standard authorship-attribution stylometry to tell machine translations made with DeepL and Google Translate from human translations. This is done using a Burrows-like distance measure procedure of cluster analysis, later visualized through network analysis. Using a corpus of French literary classics translated into English by humans and machines as illustration, this article shows that, in most cases, translations of each text were very similar irrespective of the type of translator. Discrepancies in this respect were only found between translations of authors writing in very complex style (Proust). On the other hand, sentence length distribution compared with the Dynamic Time Warping Distance method was much more indicative of whether translations were made by humans or by machines. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: © 2019 EADH: The European Association for Digital Humanities. item: Digital Scholarship in the Humanities holder: Oxford University Press / USA dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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